• Journal of Information Science Theory and Practice
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• v.11 no.1
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• pp.61-78
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• 2023

Ontological knowledge modelling of epic texts, though being an established research arena backed by concrete multilingual and multicultural works, still suffers from two key shortcomings. Firstly, all epic ontological models developed till date have been designed following ad-hoc methodologies, most often combining existing general purpose ontology development methodologies. Secondly, none of the ad-hoc methodologies consider the potential reuse of existing epic ontological models for enrichment, if available. This paper presents, as a unified solution to the above shortcomings, the design and development of GOMME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general. GOMME is grounded in transdisciplinary foundations of canonical norms for epics, knowledge modelling best practices, application satisfiability norms, and cognitive generative questions. It is also the first methodology (in epic modelling but also in general) to be flexible enough to integrate, in practice, the options of knowledge modelling via reuse or from scratch. The feasibility of GOMME is validated via a first brief implementation of ontological modelling of the Indian epic Mahabharata by reusing an existing ontology. The preliminary results are promising, with the GOMME-produced model being both ontologically thorough and competent performance-wise.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.357-365
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• 2008

Requirements traceability enables to reuse various kinds of software artifacts, which are the results from software development life cycle, rather than reuse source code only. To support requirements traceability for reuse of software artifacts, 1) artifacts should be described based on requirements and 2) a requirements tracing method should be supported. In this paper, we provide a description model for annotating requirements information to software artifacts by using requirements ontology. We also provide semantic tagging method users to efficiently annotate artifacts with the requirements ontology. And we finally present how requirements traceability is supported based on requirements ontology and also suggest the system architecture for requirements traceability support.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.949-955
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• 2009

Managing knowledge effectively is a critical factor for the competitive power of a company. There are efforts to use knowledge as an important resource in many industrial areas and likewise the interest in knowledge management is growing in the construction industry. Nevertheless, there are limitations in the current capture and reuse of knowledge in the construction industry owing to the unique characteristics of the knowledge created during the processes of projects. The knowledge produced during the processes of construction projects is project-oriented, experiential and context specific and due to these characteristics the reuse of knowledge is difficult. In this research, we focus on capturing and identifying the characteristics of construction knowledge and propose a method to apply these characteristics in developing an ontology based construction knowledge retrieval system to improve construction knowledge retrieval and enhance knowledge reuse.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.65-69
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• 2003

As complex mathematical models are increasingly adopted for business decision-making, difficulties arise in reusing solvers (i.e., model solving algorithms) against diverse models and data sets and thus the collaboration among users (model/solver builders and decision makers) in multiple departments becomes very difficult. To facilitate the solver reuse, this paper adopts the Web services technologies as the base technologies for linking the solvers to the models, both of which are created on different modeling paradigms and different system platforms, in unified system architecture. Specifically, this paper focuses on designing an ontology that represents the interfacing semantics of the model-solver interactions in a general and standardized form. By referring to the ontology, a model management system (MMS) can autonomously suggest a set of compatible solvers and apply them to individual models even though the decision makers are not knowledgeable enough about all the details of the models and the solvers. Thus, this Web services based MMS would improve the reusability of the solvers by relieving the decision makers from the risk of erroneous application of a solver to syntactically and semantically incompatible models and the burden of considerable understanding of model and solver semantics.

• Journal of Internet Computing and Services
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• v.2 no.1
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• pp.13-21
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• 2001

This paper studies on Multiple Ontology to share and reuse know1edge stored in every system under the Distributed Web Environment using idle resource connected the Internet. Multiple Ontology is to convert to be able know1edge sharing between ontologies constructed by one and the other. This study focuses on as follows; To compose the Distributed Web Ontology multiple system, Construction Method of the Distributed Web Environment and Composition of Multiple Ontology for Know1edge sharing and reuse. System Composed by using the jini technology has constructed extension of heterogeneous systems and effective web computing environment. Also, through 'distribution operation, practical know1edge exchange among Multiple Ontologies under the Distributed Web Environment has been processed excellently.

• Korea Information Processing Society Review
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• v.11 no.2
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• pp.64-75
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• 2004

90년대부터 인공지능(Artificial Intelligence)의 지식공학(knowledge engineering) 분야에서 온톨로지(Ontology)가 지식의 공유(sharing)와 재사용(reuse)관점에서 활발하게 사용되기 시작했다. 현재 온톨로지는 이런 지식공학 외에 에이전트에 기반한 소프트웨어 공학이나 전자상거래 등 여러 분야에 널리 퍼져 사용되고 있다. 그러나 그 적용범위의 다양함과 실체의 불분명함으로 인해 그 사용이 혼란스러운 것도 사실이다.(중략)

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.337-338
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• 2006

The requirement to reuse a design knowledge have been enlarged with the automation of a design system. A design knowledge gives logical and technical meanings to design data of a problem area. The representation of the knowledge is distributed and developed independently. For this reason, we need a general methodology with a semantic interoperability of design knowledge. In this paper, we accept previous requirements by using semantic query system with ontology repository.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.53-67
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• 2008

At present, for reuse of similar domains between feature model and class model. researches of transformation at the model level and of transformation using ontology between two models are being made. but consistent transformation through metamodel is not made. And the factors of modeling transformation targets are not sufficient, and especially, automatic transformation algorithm and supporting tools are not provided so reuse of domains between models is not activated. This paper proposes a method of transformation from feature model to class model using ontology on the metamodel. For this, it re-establishes the metamodel of feature model, class model, and ontology, and it defines the properties of modelling factors for each metamodel. Based on the properties, it defines the profiles of transformation rules between feature mndel and ontology, and between ontology and class model, using set theory and propositional calculus. For automation of the transformation, it creates transformation algorithm and supporting tools. Using the proposed transformation rules and tools, real application is made through Electronic Approval System. Through this, it is possible to transform from the existing constructed feature model to the class model and to use it again for a different development method. Especially, it is Possible to remove ambiguity of semantic transformation using ontology, and automation of transformation maintains consistence between models.

• International Journal of Contents
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• v.12 no.2
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• pp.24-30
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• 2016

Ontology is known to be one of the most important technologies in achieving semantic web. It is critical as it represents the knowledge in a machine readable state. World Wide Web Consortium (W3C) has been contributing to the development of ontology for the last several years. However, the recommendation of W3C left out HTML despite the massive amount of information it contains. Also, it is difficult and time consuming to keep up with all the technologies especially in the case of constructing ontology. Thus, we propose a module and methods that reuse HTML documents, extract necessary information from HTML tags and mapping it to OWL 2. We will be combining two kinds of approaches which will be the structural refinement for making an ontology skeleton and linguistic approach for adding detailed information onto the skeleton.